Tactile navigation
Jeppe
2023-05-01
Introduction
A study on driving performance and In Vehicle infotainment system has been done. The study aims to answer two questions. 1) Can the addition of tactile navigation improve driving performance 2) Which tactile navigation (if any) improves driving performance most
The first part of this statistical analysis aims to investigate the sample to answer if the data is normally distributed and if the participants’s characteristics are distributed across the four conditions. If this is not the case an effect of condition could be attributed to other personal factors of the participants (e.g. gaming experience).
Dependent variables:
- SUS
- SART
- Total Crash
- Lane Breaks
- Total Glances
- Total Glance Time
Independent variable:
- Condition (Acrylic, Bar, Combination & Touch only)
Demographics:
- Age
- Gender
- Gaming
- Drivers licence Years
- Automatic gear experience
- Car Simulator
Population
A summary of ‘Age’ and ‘Years with drivers licence’. As with the bar graph for age we have a lot of people between 20-23 and most participants have had a drivers licence for 3-5 years.
Agessummary <- summary(dataFrame[c("Age")])
Drivingsummary <- summary(dataFrame[c("YearDriversLicence")])
Metricsummary <- summary(dataFrame[c("SART", "SUS", "TotalGlanceTime", "TotalGlances", "TotalCrash", "LaneBreaks")])
Agessummary## Age
## Min. :20.0
## 1st Qu.:21.0
## Median :22.0
## Mean :22.7
## 3rd Qu.:23.0
## Max. :34.0sd(dataFrame$Age)## [1] 2.575191Drivingsummary## YearDriversLicence
## Min. : 1.833
## 1st Qu.: 3.417
## Median : 4.417
## Mean : 4.756
## 3rd Qu.: 5.417
## Max. :16.000sd(dataFrame$YearDriversLicence)## [1] 2.426197Effect due to condition
To answer the first question of whether condition have an effect on the 6 metrics we’ll create a plot of means with confidence (95%) as whiskers. According to Geoff Cumming and Sue Finch if the data has the same standard deviation it is possible to gain insight into which data could be significantly different.
Plot of means Condition/metric
plot1 <- plot1 + theme(legend.position = "none")
plot2 <- plot2 + theme(legend.position = "none")
plot3 <- plot3 + theme(legend.position = "none")
plot4 <- plot4 + theme(legend.position = "none")
plot5 <- plot5 + theme(legend.position = "none")
plot6 <- plot6 + theme(legend.position = "none")Distribution of metrics (Bar and density)
- SUS
- SART
- Total Crash
- Lane Breaks
- Total Glances
- Total Glance Time
Bar Charts
SUSTotal <- ggplot(data = dataFrame, mapping = aes(x = SUS, fill = Condition))
SUSTotal <- SUSTotal + geom_bar(col = "black", size = .3) + scale_y_continuous(name = "Count", breaks = c(0:150)) + ggtitle("SUSTotal distribution") + scale_fill_brewer(palette = Palette)
SARTTotal <- ggplot(data = dataFrame, mapping = aes(x = SART, fill = Condition))
SARTTotal <- SARTTotal + geom_bar(col = "black", size = .3) + scale_y_continuous(name = "Count", breaks = c(0:150)) + ggtitle("SARTTotal distribution") + scale_fill_brewer(palette = Palette)
TotalCrashTotal <- ggplot(data = dataFrame, mapping = aes(x = TotalCrash, fill = Condition))
TotalCrashTotal <- TotalCrashTotal + geom_bar(col = "black", size = .3) + scale_y_continuous(name = "Count", breaks = c(0:150)) + ggtitle("TotalCrashTotal distribution") + scale_fill_brewer(palette = Palette)
LaneBreakTotal <- ggplot(data = dataFrame, mapping = aes(x = LaneBreaks, fill = Condition))
LaneBreakTotal <- LaneBreakTotal + geom_bar(col = "black", size = .3) + scale_y_continuous(name = "Count", breaks = c(0:150)) + ggtitle("LaneBreakTotal distribution") + scale_fill_brewer(palette = Palette)
GlancesTotal <- ggplot(data = dataFrame, mapping = aes(x = TotalGlances, fill = Condition))
GlancesTotal <- GlancesTotal + geom_bar(col = "black", size = .3) + scale_y_continuous(name = "Count", breaks = c(0:150)) + ggtitle("GlancesTotal distribution") + scale_fill_brewer(palette = Palette)
GlanceTimeTotal <- ggplot(data = dataFrame, mapping = aes(x = TotalGlanceTime, fill = Condition))
GlanceTimeTotal <- GlanceTimeTotal + geom_bar(col = "black", size = .3) + scale_y_continuous(name = "Count", breaks = c(0:150)) + ggtitle("SUGlanceTimeTotalSTotal distribution") + scale_fill_brewer(palette = Palette)
SUSTotal
SARTTotal
TotalCrashTotal
LaneBreakTotal
GlancesTotal
GlanceTimeTotal
Density
alpha <- 0.4
SUSDistribution <- ggplot(data = dataFrame, mapping = aes(x = SUS, fill = Condition, alpha = alpha))
SUSDistribution <- SUSDistribution + geom_density() + ggtitle("SUS distribution") + scale_fill_brewer(palette = Palette)
SUSDistribution <- ggplot(data = dataFrame, mapping = aes(x = SART, fill = Condition, alpha = alpha))
SUSDistribution <- SUSDistribution + geom_density() + ggtitle("SART distribution") + scale_fill_brewer(palette = Palette)
CrashDistribution <- ggplot(data = dataFrame, mapping = aes(x = TotalCrash, fill = Condition, alpha = alpha))
CrashDistribution <- CrashDistribution + geom_density() + ggtitle("Total Crash distribution") + scale_fill_brewer(palette = Palette)
LaneBreakDistribution <- ggplot(data = dataFrame, mapping = aes(x = LaneBreaks, fill = Condition, alpha = alpha))
LaneBreakDistribution <- LaneBreakDistribution + geom_density() + ggtitle("Lane Break distribution") + scale_fill_brewer(palette = Palette)
GlancesDistribution <- ggplot(data = dataFrame, mapping = aes(x = TotalGlances, fill = Condition, alpha = alpha))
GlancesDistribution <- GlancesDistribution + geom_density() + ggtitle("Glances distribution") + scale_fill_brewer(palette = Palette)
GlanceTimeDistribution <- ggplot(data = dataFrame, mapping = aes(x = TotalGlanceTime, fill = Condition, alpha = alpha))
GlanceTimeDistribution <- GlanceTimeDistribution + geom_density() + ggtitle("Total Glance Time distribution") + scale_fill_brewer(palette = Palette)
plot1<- SUSTotal
plot2<-SARTTotal
plot3<-TotalCrashTotal
plot4<-LaneBreakTotal
plot5<-GlancesTotal
plot6<-GlanceTimeTotal
GlancetimeAndGlanceCount <- ggplot(dataFrame, aes(x = TotalGlances, y = TotalGlanceTime, color = Condition)) +
geom_point(size = 2, shape = 23) +
geom_smooth(method = lm, se = FALSE)Statistical analysis
Statistics will be run on all driving performance metrics grouped by conditions, to look for significant main effects. The normality and the variance will be analysed to ensure the correct statistical test is being used. Sharpiro Wilk test will be used to analyse the distribution of the residuals. Bartlett’s test will be used in case of normality to analyse the variance Levenes test will be used in case the residuals are not normal distributed to analyse the variance
Analysis of Glance Time grouped by conditions
The residuals proved to be normally distributed and the data has homogene variance which means a standard ANOVA test can be used
glanceTimeData <- subset(dataFrame, select = c("Participants", "Condition", "TotalGlanceTime"))
summary(glanceTimeData)## Participants Condition TotalGlanceTime
## P1 : 1 Acrylic :11 Min. :21.06
## P10 : 1 Bar :11 1st Qu.:28.71
## P11 : 1 Combination:11 Median :36.77
## P12 : 1 Touch_only :11 Mean :36.93
## P13 : 1 3rd Qu.:42.32
## P14 : 1 Max. :60.01
## (Other):38get_summary_stats(glanceTimeData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TotalGlanc… 44 21.1 60.0 36.8 28.7 42.3 13.6 11.1 36.9 9.88 1.49
## # ℹ 1 more variable: ci <dbl>glanceTimeData %>%
group_by(Condition) %>%
identify_outliers(TotalGlanceTime)## # A tibble: 3 × 5
## Condition Participants TotalGlanceTime is.outlier is.extreme
## <fct> <fct> <dbl> <lgl> <lgl>
## 1 Touch_only P3 59.2 TRUE FALSE
## 2 Touch_only P4 22.5 TRUE FALSE
## 3 Touch_only P9 24.0 TRUE FALSEglanceTimeModel <- lm(TotalGlanceTime ~ Condition, data = glanceTimeData)
ggqqplot(residuals(glanceTimeModel))
shapiro_test(residuals(glanceTimeModel))## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(glanceTimeModel) 0.970 0.302# We use a bartlett test as the residuals are normally distributed
bartlett.test(TotalGlanceTime ~ Condition, data = glanceTimeData)##
## Bartlett test of homogeneity of variances
##
## data: TotalGlanceTime by Condition
## Bartlett's K-squared = 1.2274, df = 3, p-value = 0.7464glanceTimeTest <- glanceTimeData %>% anova_test(TotalGlanceTime ~ Condition)
glanceTimeTest## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Condition 3 40 0.953 0.424 0.067Analysis of Glance Count grouped by conditions
glanceCountData <- subset(dataFrame, select = c("Participants", "Condition", "TotalGlances"))
summary(glanceCountData)## Participants Condition TotalGlances
## P1 : 1 Acrylic :11 Min. :29.00
## P10 : 1 Bar :11 1st Qu.:41.00
## P11 : 1 Combination:11 Median :48.50
## P12 : 1 Touch_only :11 Mean :48.18
## P13 : 1 3rd Qu.:54.25
## P14 : 1 Max. :80.00
## (Other):38get_summary_stats(glanceCountData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TotalGlanc… 44 29 80 48.5 41 54.2 13.2 11.1 48.2 10.7 1.62
## # ℹ 1 more variable: ci <dbl>glanceCountData %>%
group_by(Condition) %>%
identify_outliers(TotalGlances)## [1] Condition Participants TotalGlances is.outlier is.extreme
## <0 rows> (or 0-length row.names)glanceCountModel <- lm(TotalGlances ~ Condition, data = glanceCountData)
ggqqplot(residuals(glanceCountModel))
shapiro_test(residuals(glanceCountModel))## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(glanceCountModel) 0.982 0.730bartlett.test(TotalGlances ~ Condition, data = glanceCountData)##
## Bartlett test of homogeneity of variances
##
## data: TotalGlances by Condition
## Bartlett's K-squared = 8.0415, df = 3, p-value = 0.04516glanceCountTest <- glanceCountData %>% welch_anova_test(TotalGlances ~ Condition)
glanceCountTest## # A tibble: 1 × 7
## .y. n statistic DFn DFd p method
## * <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 TotalGlances 44 1.44 3 21.4 0.26 Welch ANOVAAnalysis of Crash Count grouped by condition
crashData <- subset(dataFrame, select = c("Participants", "Condition", "TotalCrash"))
summary(crashData)## Participants Condition TotalCrash
## P1 : 1 Acrylic :11 Min. :0.0
## P10 : 1 Bar :11 1st Qu.:0.0
## P11 : 1 Combination:11 Median :0.0
## P12 : 1 Touch_only :11 Mean :0.5
## P13 : 1 3rd Qu.:1.0
## P14 : 1 Max. :3.0
## (Other):38get_summary_stats(crashData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TotalCrash 44 0 3 0 0 1 1 0 0.5 0.699 0.105
## # ℹ 1 more variable: ci <dbl>crashData %>%
group_by(Condition) %>%
identify_outliers(TotalCrash)## # A tibble: 1 × 5
## Condition Participants TotalCrash is.outlier is.extreme
## <fct> <fct> <int> <lgl> <lgl>
## 1 Touch_only P17 3 TRUE FALSEcrashDataModel <- lm(TotalCrash ~ Condition, data = crashData)
ggqqplot(residuals(crashDataModel))
shapiro_test(residuals(crashDataModel))## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(crashDataModel) 0.740 0.000000182crashData %>% levene_test(TotalCrash ~ Condition)## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 40 0.0580 0.981crashDataTest <- crashData %>% kruskal_test(TotalCrash ~ Condition)
crashDataTest## # A tibble: 1 × 6
## .y. n statistic df p method
## * <chr> <int> <dbl> <int> <dbl> <chr>
## 1 TotalCrash 44 0.203 3 0.977 Kruskal-WallisCrash Count visualization
Another way to present the crash data would be to score crashes pr
condition:
It can be seen on this graph that each condition has about the same
total crashes and some conditions have one or two participants
responsible for extra crashes.
Analysis of Lane Breaks grouped by condition
laneData <- subset(dataFrame, select = c("Participants", "Condition", "LaneBreaks"))
summary(laneData)## Participants Condition LaneBreaks
## P1 : 1 Acrylic :11 Min. : 0.000
## P10 : 1 Bar :11 1st Qu.: 2.000
## P11 : 1 Combination:11 Median : 3.000
## P12 : 1 Touch_only :11 Mean : 3.955
## P13 : 1 3rd Qu.: 6.000
## P14 : 1 Max. :15.000
## (Other):38get_summary_stats(laneData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LaneBreaks 44 0 15 3 2 6 4 2.96 3.96 3.25 0.49
## # ℹ 1 more variable: ci <dbl>laneData %>%
group_by(Condition) %>%
identify_outliers(LaneBreaks)## # A tibble: 2 × 5
## Condition Participants LaneBreaks is.outlier is.extreme
## <fct> <fct> <int> <lgl> <lgl>
## 1 Acrylic P28 10 TRUE FALSE
## 2 Bar P35 15 TRUE FALSElaneDataModel <- lm(LaneBreaks ~ Condition, data = laneData)
ggqqplot(residuals(laneDataModel))
shapiro_test(residuals(laneDataModel))## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(laneDataModel) 0.923 0.00582laneData %>% levene_test(LaneBreaks ~ Condition)## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 40 0.461 0.711laneDataTest <- laneData %>% kruskal_test(LaneBreaks ~ Condition)
laneDataTest## # A tibble: 1 × 6
## .y. n statistic df p method
## * <chr> <int> <dbl> <int> <dbl> <chr>
## 1 LaneBreaks 44 0.234 3 0.972 Kruskal-WallisAnalysis of SUS grouped by condition
susData <- subset(dataFrame, select = c("Participants", "Condition", "SUS"))
summary(susData)## Participants Condition SUS
## P1 : 1 Acrylic :11 Min. :47.50
## P10 : 1 Bar :11 1st Qu.:73.75
## P11 : 1 Combination:11 Median :82.50
## P12 : 1 Touch_only :11 Mean :79.26
## P13 : 1 3rd Qu.:87.50
## P14 : 1 Max. :95.00
## (Other):38get_summary_stats(susData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SUS 44 47.5 95 82.5 73.8 87.5 13.8 11.1 79.3 12.1 1.83
## # ℹ 1 more variable: ci <dbl>susData %>%
group_by(Condition) %>%
identify_outliers(SUS)## # A tibble: 2 × 5
## Condition Participants SUS is.outlier is.extreme
## <fct> <fct> <dbl> <lgl> <lgl>
## 1 Combination P15 65 TRUE FALSE
## 2 Combination P43 50 TRUE TRUEsusDataModel <- lm(SUS ~ Condition, data = susData)
ggqqplot(residuals(susDataModel))
shapiro_test(residuals(susDataModel))## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(susDataModel) 0.901 0.00116susData %>% levene_test(SUS ~ Condition)## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 40 0.214 0.886susDataTest <- susData %>% kruskal_test(SUS ~ Condition)
susDataTest## # A tibble: 1 × 6
## .y. n statistic df p method
## * <chr> <int> <dbl> <int> <dbl> <chr>
## 1 SUS 44 1.97 3 0.578 Kruskal-WallisAnalysis of SART grouped by condition
sartData <- subset(dataFrame, select = c("Participants", "Condition", "SART"))
sartData <- sartData[!(sartData$Participants == "P12" | sartData$Participants == "P13" | sartData$Participants == "P29" | sartData$Participants == "P30"), ]
summary(sartData)## Participants Condition SART
## P1 : 1 Acrylic :10 Min. : 4.00
## P10 : 1 Bar :10 1st Qu.:12.00
## P11 : 1 Combination:10 Median :15.00
## P14 : 1 Touch_only :10 Mean :14.32
## P15 : 1 3rd Qu.:17.00
## P16 : 1 Max. :25.00
## (Other):34get_summary_stats(sartData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SART 40 4 25 15 12 17 5 4.45 14.3 4.34 0.686
## # ℹ 1 more variable: ci <dbl>sartData %>%
group_by(Condition) %>%
identify_outliers(SART)## # A tibble: 3 × 5
## Condition Participants SART is.outlier is.extreme
## <fct> <fct> <int> <lgl> <lgl>
## 1 Combination P7 20 TRUE FALSE
## 2 Combination P20 21 TRUE FALSE
## 3 Touch_only P17 4 TRUE FALSEsartDataModel <- lm(SART ~ Condition, data = sartData)
ggqqplot(residuals(sartDataModel))
shapiro_test(residuals(sartDataModel))## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(sartDataModel) 0.988 0.947bartlett.test(SART ~ Condition, data = sartData)##
## Bartlett test of homogeneity of variances
##
## data: SART by Condition
## Bartlett's K-squared = 1.3196, df = 3, p-value = 0.7245sartDataTest <- sartData %>% anova_test(SART ~ Condition)
sartDataTest## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Condition 3 36 0.133 0.94 0.011Plots of Residuals
plot(residuals(glanceTimeModel))
plot(residuals(glanceCountModel))
plot(residuals(crashDataModel))
plot(residuals(laneDataModel))
plot(residuals(susDataModel))
plot(residuals(sartDataModel))
plot(glanceTimeData$Condition,residuals(glanceTimeModel))
plot(glanceCountData$Condition,residuals(glanceCountModel))
plot(crashData$Condition,residuals(crashDataModel))
plot(laneData$Condition,residuals(laneDataModel))
plot(susData$Condition,residuals(susDataModel))
plot(sartData$Condition,residuals(sartDataModel))
Analysis of use of aid grouped by condition
aidData <- subset(dataFrame, select = c("Participants", "Condition", "UseOfAid"))
aidData <- aidData[!(aidData$Condition == "Touch_only"), ]
summary(aidData)## Participants Condition UseOfAid
## P1 : 1 Acrylic :11 Min. :0.0000
## P11 : 1 Bar :11 1st Qu.:0.0000
## P12 : 1 Combination:11 Median :0.0000
## P14 : 1 Touch_only : 0 Mean :0.8182
## P15 : 1 3rd Qu.:1.0000
## P19 : 1 Max. :4.0000
## (Other):27get_summary_stats(aidData)## # A tibble: 1 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 UseOfAid 33 0 4 0 0 1 1 0 0.818 1.33 0.232
## # ℹ 1 more variable: ci <dbl>aidPlotData <- subset(aidData, UseOfAid > 0)
aidPlot <- ggplot(data = aidPlotData, mapping = aes(x = Condition, y = UseOfAid, fill = Condition, label = UseOfAid))
aidPlot + geom_col(col = "black") +
scale_y_continuous(name = "UseOfAid", breaks = c(0:15)) + ggtitle("Use of aid ") +
geom_text(size = 6, position = position_stack(vjust = 0.5))
Plot of means - All Dependent variables vs All Independent variables
To investigate weather differences in demographics have had an influence on conditions a plot of means for each metric will be performed with each independent variable. This will be done with wiskers as 95% confidence interval and if the data has homoscedasticity it could be possible to eyeball significant influences. If these are detected we’ll perform a t-test
Dependent variables:
- SUS
- SART
- Total Crash
- Lane Breaks
- Total Glances
- Total Glance Time
Independent variables:
- Condition
- Gender
- Gaming
- Drivers licence (This plot will have a linear model underneath it
(x=Years with drivers license y=Dependent variable)) - Automatic
gear
- Car Simulator
SUS
SaveSixPlots(Metric = "SUS")## [[1]]
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## Call:
## lm(formula = y ~ x, data = dataFrame)
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## Residuals:
## Min 1Q Median 3Q Max
## -30.736 -5.481 3.101 9.028 16.407
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.8139 4.0829 20.04 <2e-16 ***
## x -0.5367 0.7666 -0.70 0.488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 12.2 on 42 degrees of freedom
## Multiple R-squared: 0.01154, Adjusted R-squared: -0.012
## F-statistic: 0.4903 on 1 and 42 DF, p-value: 0.4877
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Sart
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## lm(formula = y ~ x, data = dataFrame)
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## Residuals:
## Min 1Q Median 3Q Max
## -11.126 -2.709 0.331 2.755 10.092
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.7029 1.4170 11.082 4.78e-14 ***
## x -0.2386 0.2660 -0.897 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 4.232 on 42 degrees of freedom
## Multiple R-squared: 0.01879, Adjusted R-squared: -0.004571
## F-statistic: 0.8044 on 1 and 42 DF, p-value: 0.3749
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### Total crash
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## lm(formula = y ~ x, data = dataFrame)
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## Residuals:
## Min 1Q Median 3Q Max
## -0.6321 -0.4933 -0.4428 0.4963 2.5728
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35204 0.23534 1.496 0.142
## x 0.03111 0.04419 0.704 0.485
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## Residual standard error: 0.703 on 42 degrees of freedom
## Multiple R-squared: 0.01167, Adjusted R-squared: -0.01186
## F-statistic: 0.4958 on 1 and 42 DF, p-value: 0.4852
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Lane Breaks
SaveSixPlots("LaneBreaks")## [[1]]
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## lm(formula = y ~ x, data = dataFrame)
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## Residuals:
## Min 1Q Median 3Q Max
## -4.4578 -2.0947 -0.8649 1.9498 11.3722
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3907 1.0963 3.093 0.00352 **
## x 0.1186 0.2058 0.576 0.56764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.275 on 42 degrees of freedom
## Multiple R-squared: 0.007839, Adjusted R-squared: -0.01578
## F-statistic: 0.3319 on 1 and 42 DF, p-value: 0.5676
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Total Glances
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## lm(formula = y ~ x, data = dataFrame)
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## Residuals:
## Min 1Q Median 3Q Max
## -19.050 -7.358 0.167 6.005 31.550
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.1331 3.6376 13.507 <2e-16 ***
## x -0.2000 0.6829 -0.293 0.771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.87 on 42 degrees of freedom
## Multiple R-squared: 0.002038, Adjusted R-squared: -0.02172
## F-statistic: 0.08578 on 1 and 42 DF, p-value: 0.7711
##
##
## [[6]]
##
## [[7]]
### Total Glance time
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## Call:
## lm(formula = y ~ x, data = dataFrame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.3769 -8.3684 0.1266 5.2345 21.8988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.8529 3.3289 11.671 9.15e-15 ***
## x -0.4046 0.6250 -0.647 0.521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.944 on 42 degrees of freedom
## Multiple R-squared: 0.009878, Adjusted R-squared: -0.0137
## F-statistic: 0.419 on 1 and 42 DF, p-value: 0.5209
##
##
## [[6]]
##
## [[7]]
From the many plot of means with confidence interval 95% as wisksers we’ve determined to test the following interactions
LaneBreaksAutomatic <- t.test(formula = LaneBreaks ~ Automatic, data = dataFrame)
SartGender <- t.test(formula = SART ~ Gender, data = dataFrame)
CrashComputer <- kruskal.test(formula = TotalCrash ~ ComputerGames, data = dataFrame)
CrashAutomatic <- t.test(formula = TotalCrash ~ Automatic, data = dataFrame)
GlanceTimeAutomatic <- t.test(formula = TotalGlanceTime ~ Automatic, data = dataFrame)
GlancesGender <- t.test(formula = TotalGlances ~ Gender, data = dataFrame)
GlancesAutomatic <- t.test(formula = TotalGlances ~ Automatic, data = dataFrame)
# Måske er der noget med glances og condition
LaneBreaksAutomatic##
## Welch Two Sample t-test
##
## data: LaneBreaks by Automatic
## t = 2.6531, df = 17.133, p-value = 0.01666
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## 0.618242 5.406572
## sample estimates:
## mean in group No mean in group Yes
## 6.076923 3.064516SartGender##
## Welch Two Sample t-test
##
## data: SART by Gender
## t = -2.4169, df = 17.261, p-value = 0.02699
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## -5.9791715 -0.4090638
## sample estimates:
## mean in group F mean in group M
## 12.10000 15.29412CrashComputer##
## Kruskal-Wallis rank sum test
##
## data: TotalCrash by ComputerGames
## Kruskal-Wallis chi-squared = 2.2623, df = 2, p-value = 0.3227CrashAutomatic##
## Welch Two Sample t-test
##
## data: TotalCrash by Automatic
## t = 1.7097, df = 14.508, p-value = 0.1086
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## -0.1230151 1.1056453
## sample estimates:
## mean in group No mean in group Yes
## 0.8461538 0.3548387GlanceTimeAutomatic##
## Welch Two Sample t-test
##
## data: TotalGlanceTime by Automatic
## t = 2.953, df = 23.453, p-value = 0.007043
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## 2.626666 14.871746
## sample estimates:
## mean in group No mean in group Yes
## 43.09308 34.34387GlancesGender##
## Welch Two Sample t-test
##
## data: TotalGlances by Gender
## t = 1.8374, df = 24.153, p-value = 0.07848
## alternative hypothesis: true difference in means between group F and group M is not equal to 0
## 95 percent confidence interval:
## -0.6707051 11.5883521
## sample estimates:
## mean in group F mean in group M
## 52.40000 46.94118GlancesAutomatic##
## Welch Two Sample t-test
##
## data: TotalGlances by Automatic
## t = 1.8943, df = 20.358, p-value = 0.07249
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## -0.6836275 14.3610468
## sample estimates:
## mean in group No mean in group Yes
## 53.00000 46.16129When analysing this much data the risk of p-hacking increases. We’ll only use these significanses if there is a big heteroscedasticity in regards to the four conditions maybe this change can explain
The three significant effects were:
1) LaneBreaks by Automatic 0.01666
2) TotalGlanceTime by Automatic 0.007043
3) SART by Gender 0.02699
We’ll see if these effects have been equally distributed across the four conditions:
AutomaticExperienceCondition
Automatic experience is distributed quite nice with a difference of 2
between highest and lowest. Therefore this effect is irrelevant.
GenderCondition
Gender is distributed with a difference of 1 between highest and lowest.
Therefore this effect is irrelevant
To test significance we will use ANOVA
ANOVA has Three assumptions
1) Normality
2) Homogenity
3) Independence
The assumption of Independence is meet as our participants haven’t
influence each other in any way we know of. The participants were
instructed not to speak with each other between trials and we can hope
they have met this instruction.
Analysis of glance duration per task
Another interesting aspect to look at is the distribution of glance duration across all tasks. This could potentially reveal patterns in certain tasks taking longer on average, indicating an imbalance in difficulty among the tasks
The glance durations are analysed with regards to guidelines found for IVIS designs - a single glance should be shorter than 1.5 seconds - the total time with eyes off the road should be less than 12 seconds for each task
percentTooLongGlances <- (sum(taskLongDataFrame$Single_Glance_duration >= 1.5) / sum(taskLongDataFrame$Single_Glance_duration > 0)) * 100
percentTooLongGlances## [1] 5.09434percentTooLongTasks <- (sum(taskShortDataFrame$Total_glance_duration_pr_task >= 12) / sum(taskShortDataFrame$Total_glance_duration_pr_task > 0)) * 100
percentTooLongTasks## [1] 2.283105singleGlanceBox <- ggplot(taskLongDataFrame, aes(x = Condition, y = Single_Glance_duration, fill = Condition)) +
geom_boxplot() +
scale_fill_brewer(palette = "Spectral")
singleGlanceBox
glanceTaskBox <- ggplot(taskShortDataFrame, aes(x = Task_nr, y = Total_glance_duration_pr_task, group = Task_nr, fill = factor(Task_nr))) +
geom_boxplot() +
scale_x_continuous(breaks = seq(0, 10, by = 1)) +
scale_fill_brewer(palette = "Spectral")
glanceTaskBox